High-Performance Predictive Analytics for Genomic Medicine Using GPU and ML

EasyChair Preprint no. 14038

Abstract

Genomic medicine has transformed healthcare by leveraging vast datasets to personalize treatment and predict disease susceptibility. High-performance computing, particularly Graphics Processing Units (GPUs), combined with Machine Learning (ML), offers unprecedented speed and efficiency in analyzing genomic data. This paper explores the integration of GPU-accelerated algorithms with ML techniques to enhance predictive analytics in genomic medicine. We review the application of GPU computing in accelerating genomic data preprocessing, feature extraction, and model training. Furthermore, we discuss case studies illustrating the efficacy of GPU-enhanced models in predicting disease risks, identifying biomarkers, and optimizing treatment strategies. Insights gained underscore the pivotal role of GPU-accelerated ML in advancing genomic medicine towards more precise, personalized healthcare interventions.

Keyphrases: Bioinformatic algorithms, Deep learning in bioinformatics, GPU-based bioinformatics, High Performance Computing

@Booklet{EasyChair:14038,
  author = {Abi Cit},
  title = {High-Performance Predictive Analytics for Genomic Medicine Using GPU and ML},
  howpublished = {EasyChair Preprint no. 14038},

  year = {EasyChair, 2024}}